CVE-2026-24268 in TensorRT
Summary
by MITRE • 07/15/2026
NVIDIA TensorRT contains a vulnerability where an attacker might cause a heap-based buffer overflow. A successful exploit of this vulnerability might lead to code execution.
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Analysis
by VulDB Data Team • 07/15/2026
NVIDIA TensorRT represents a powerful inference runtime for deep learning models that has become widely adopted across the artificial intelligence ecosystem, particularly in edge computing and data center deployments. The vulnerability resides within the memory management mechanisms of this software library, specifically manifesting as a heap-based buffer overflow condition that can be triggered through malformed input processing. This type of vulnerability is particularly dangerous because it can be exploited to execute arbitrary code on systems running affected versions of TensorRT, potentially compromising the entire underlying infrastructure where these components operate.
The technical flaw occurs when the software processes input data without proper bounds checking during heap allocation operations within the inference engine. Attackers can craft malicious inputs that exceed the allocated buffer boundaries, causing adjacent memory regions to be overwritten and potentially allowing for stack smashing or memory corruption attacks. This heap-based overflow can be leveraged through various attack vectors including model loading operations, input tensor processing, or parameter parsing within the deep learning inference pipeline. The vulnerability aligns with CWE-122 which specifically addresses heap-based buffer overflows, representing one of the most prevalent and dangerous classes of memory safety issues in software systems.
The operational impact of this vulnerability extends far beyond simple denial-of-service conditions, as successful exploitation can result in complete system compromise and unauthorized code execution. Organizations deploying NVIDIA TensorRT for production workloads face significant risk exposure, particularly in cloud environments where multiple users might interact with the inference engines or when models are loaded from untrusted sources. The attack surface is broad since TensorRT is used across various applications including autonomous vehicles, medical imaging systems, financial services, and security surveillance platforms where the consequences of code execution can be catastrophic.
Mitigation strategies should focus on immediate patch application from NVIDIA as recommended in their security advisories, alongside comprehensive input validation and sandboxing measures. Organizations should implement network segmentation to limit access to TensorRT components and establish robust monitoring for anomalous behavior patterns that might indicate exploitation attempts. The vulnerability demonstrates the critical importance of memory safety in AI infrastructure components and aligns with ATT&CK technique T1059 which covers command and script injection, as attackers may attempt to leverage such vulnerabilities to execute malicious payloads within AI processing environments.
Additional defensive measures include regular security assessments of AI deployment pipelines, implementation of automated input sanitization protocols, and establishment of secure coding practices for any custom extensions or modifications to TensorRT components. Organizations should also consider implementing runtime protection mechanisms such as address space layout randomization and stack canaries to reduce exploitability even if the underlying vulnerability cannot be immediately patched across all systems. The incident underscores the necessity of maintaining up-to-date security practices in AI infrastructure, particularly given the increasing reliance on deep learning frameworks for mission-critical applications where traditional software vulnerabilities can have amplified consequences.